Scikit-decide

# hub.solver.do_solver.do_solver_scheduling

Domain specification

Domain

# SolvingMethod

Type of discrete-optimization algorithm to use

# CP SolvingMethod

solve scheduling problem with constraint programming solver

# GA SolvingMethod

solve scheduling problem with genetic algorithm

# LNS_CP SolvingMethod

solve scheduling problem with large neighborhood search + CP solver

# LNS_LP SolvingMethod

solve scheduling problem with large neighborhood search + LP solver

# LP SolvingMethod

solve scheduling problem with linear programming solver

# LS SolvingMethod

solve scheduling problem with local search algorithm (hill climber or simulated annealing)

# PILE SolvingMethod

solve scheduling problem with greedy queue method

# build_solver

build_solver(
  solving_method: Optional[SolvingMethod],
  solver_type: Optional[type[SolverDO]],
  do_domain: Problem
) -> tuple[type[SolverDO], dict[str, Any]]

Build the discrete-optimization solver for a given solving method

# Parameters

  • solving_method: method of the solver (enum)
  • solver_type: potentially a solver class already specified by the do_solver
  • do_domain: discrete-opt problem to solve.

# Returns

A class of do-solver, associated with some default parameters to be passed to its constructor and solve function (and potentially init_model function)

# from_solution_to_policy

from_solution_to_policy(
  solution: Union[RcpspSolution, MultiskillRcpspSolution, VariantMultiskillRcpspSolution],
  domain: SchedulingDomain,
  policy_method_params: PolicyMethodParams
) -> PolicyRCPSP

Create a PolicyRCPSP object (a skdecide policy) from a scheduling solution from the discrete-optimization library.

# DOSolver

Wrapper of discrete-optimization solvers for scheduling problems

# Attributes

  • policy_method_params: params for the returned policy.
  • method: method of the discrete-optim solver used
  • solver_type: direct method class of do solver (will be used instead of method if solver_type is not None)
  • dict_params: specific params passed to the do-solver
  • callback: scikit-decide callback to be called inside do-solver when relevant.

# Constructor DOSolver

DOSolver(
  domain_factory: Callable[[], Domain],
  policy_method_params: Optional[PolicyMethodParams] = None,
  method: Optional[SolvingMethod] = None,
  do_solver_type: Optional[type[SolverDO]] = None,
  dict_params: Optional[dict[Any, Any]] = None,
  callback: Callable[[DOSolver], bool] = <lambda function>,
  policy_method_params_kwargs: Optional[dict[str, Any]] = None
)

# Parameters

  • domain_factory: A callable with no argument returning the domain to solve (can be a mere domain class). The resulting domain will be auto-cast to the level expected by the solver.

# autocast Solver

autocast(
  self,
  domain_cls: Optional[type[Domain]] = None
) -> None

Autocast itself to the level corresponding to the given domain class.

# Parameters

  • domain_cls: the domain class to which level the solver needs to autocast itself. By default, use the original domain factory passed to its constructor.

# check_domain Solver

check_domain(
  domain: Domain
) -> bool

Check whether a domain is compliant with this solver type.

By default, Solver.check_domain() provides some boilerplate code and internally calls Solver._check_domain_additional() (which returns True by default but can be overridden to define specific checks in addition to the "domain requirements"). The boilerplate code automatically checks whether all domain requirements are met.

# Parameters

  • domain: The domain to check.

# Returns

True if the domain is compliant with the solver type (False otherwise).

# complete_with_default_hyperparameters Hyperparametrizable

complete_with_default_hyperparameters(
  kwargs: dict[str, Any],
  names: Optional[list[str]] = None
)

Add missing hyperparameters to kwargs by using default values

Args: kwargs: keyword arguments to complete (e.g. for __init__, init_model, or solve) names: names of the hyperparameters to add if missing. By default, all available hyperparameters.

Returns: a new dictionary, completion of kwargs

# copy_and_update_hyperparameters Hyperparametrizable

copy_and_update_hyperparameters(
  names: Optional[list[str]] = None,
  **kwargs_by_name: dict[str, Any]
) -> list[Hyperparameter]

Copy hyperparameters definition of this class and update them with specified kwargs.

This is useful to define hyperparameters for a child class for which only choices of the hyperparameter change for instance.

Args: names: names of hyperparameters to copy. Default to all. **kwargs_by_name: for each hyperparameter specified by its name, the attributes to update. If a given hyperparameter name is not specified, the hyperparameter is copied without further update.

Returns:

# get_default_hyperparameters Hyperparametrizable

get_default_hyperparameters(
  names: Optional[list[str]] = None
) -> dict[str, Any]

Get hyperparameters default values.

Args: names: names of the hyperparameters to choose. By default, all available hyperparameters will be suggested.

Returns: a mapping between hyperparameter's name_in_kwargs and its default value (None if not specified)

# get_domain_requirements Solver

get_domain_requirements(
) -> list[type]

Get domain requirements for this solver class to be applicable.

Domain requirements are classes from the skdecide.builders.domain package that the domain needs to inherit from.

# Returns

A list of classes to inherit from.

# get_hyperparameter Hyperparametrizable

get_hyperparameter(
  name: str
) -> Hyperparameter

Get hyperparameter from given name.

# get_hyperparameters_by_name Hyperparametrizable

get_hyperparameters_by_name(
) -> dict[str, Hyperparameter]

Mapping from name to corresponding hyperparameter.

# get_hyperparameters_names Hyperparametrizable

get_hyperparameters_names(
) -> list[str]

List of hyperparameters names.

# get_next_action DeterministicPolicies

get_next_action(
  self,
  observation: StrDict[D.T_observation]
) -> StrDict[list[D.T_event]]

Get the next deterministic action (from the solver's current policy).

# Parameters

  • observation: The observation for which next action is requested.

# Returns

The next deterministic action.

# get_next_action_distribution UncertainPolicies

get_next_action_distribution(
  self,
  observation: StrDict[D.T_observation]
) -> Distribution[StrDict[list[D.T_event]]]

Get the probabilistic distribution of next action for the given observation (from the solver's current policy).

# Parameters

  • observation: The observation to consider.

# Returns

The probabilistic distribution of next action.

# is_policy_defined_for Policies

is_policy_defined_for(
  self,
  observation: StrDict[D.T_observation]
) -> bool

Check whether the solver's current policy is defined for the given observation.

# Parameters

  • observation: The observation to consider.

# Returns

True if the policy is defined for the given observation memory (False otherwise).

# reset Solver

reset(
  self
) -> None

Reset whatever is needed on this solver before running a new episode.

This function does nothing by default but can be overridden if needed (e.g. to reset the hidden state of a LSTM policy network, which carries information about past observations seen in the previous episode).

# sample_action Policies

sample_action(
  self,
  observation: StrDict[D.T_observation]
) -> StrDict[list[D.T_event]]

Sample an action for the given observation (from the solver's current policy).

# Parameters

  • observation: The observation for which an action must be sampled.

# Returns

The sampled action.

# solve FromInitialState

solve(
  self
) -> None

Run the solving process.

After solving by calling self._solve(), autocast itself so that rollout methods apply to the domain original characteristics.

TIP

The nature of the solutions produced here depends on other solver's characteristics like policy and assessibility.

# suggest_hyperparameter_with_optuna Hyperparametrizable

suggest_hyperparameter_with_optuna(
  trial: optuna.trial.Trial,
  name: str,
  prefix: str,
  **kwargs
) -> Any

Suggest hyperparameter value during an Optuna trial.

This can be used during Optuna hyperparameters tuning.

Args: trial: optuna trial during hyperparameters tuning name: name of the hyperparameter to choose prefix: prefix to add to optuna corresponding parameter name (useful for disambiguating hyperparameters from subsolvers in case of meta-solvers) **kwargs: options for optuna hyperparameter suggestions

Returns:

kwargs can be used to pass relevant arguments to

  • trial.suggest_float()
  • trial.suggest_int()
  • trial.suggest_categorical()

For instance it can

  • add a low/high value if not existing for the hyperparameter or override it to narrow the search. (for float or int hyperparameters)
  • add a step or log argument (for float or int hyperparameters, see optuna.trial.Trial.suggest_float())
  • override choices for categorical or enum parameters to narrow the search

# suggest_hyperparameters_with_optuna Hyperparametrizable

suggest_hyperparameters_with_optuna(
  trial: optuna.trial.Trial,
  names: Optional[list[str]] = None,
  kwargs_by_name: Optional[dict[str, dict[str, Any]]] = None,
  fixed_hyperparameters: Optional[dict[str, Any]] = None,
  prefix: str
) -> dict[str, Any]

Suggest hyperparameters values during an Optuna trial.

Args: trial: optuna trial during hyperparameters tuning names: names of the hyperparameters to choose. By default, all available hyperparameters will be suggested. If fixed_hyperparameters is provided, the corresponding names are removed from names. kwargs_by_name: options for optuna hyperparameter suggestions, by hyperparameter name fixed_hyperparameters: values of fixed hyperparameters, useful for suggesting subbrick hyperparameters, if the subbrick class is not suggested by this method, but already fixed. Will be added to the suggested hyperparameters. prefix: prefix to add to optuna corresponding parameters (useful for disambiguating hyperparameters from subsolvers in case of meta-solvers)

Returns: mapping between the hyperparameter name and its suggested value. If the hyperparameter has an attribute name_in_kwargs, this is used as the key in the mapping instead of the actual hyperparameter name. the mapping is updated with fixed_hyperparameters.

kwargs_by_name[some_name] will be passed as **kwargs to suggest_hyperparameter_with_optuna(name=some_name)

# _check_domain_additional Solver

_check_domain_additional(
  domain: Domain
) -> bool

Check whether the given domain is compliant with the specific requirements of this solver type (i.e. the ones in addition to "domain requirements").

This is a helper function called by default from Solver.check_domain(). It focuses on specific checks, as opposed to taking also into account the domain requirements for the latter.

# Parameters

  • domain: The domain to check.

# Returns

True if the domain is compliant with the specific requirements of this solver type (False otherwise).

# _cleanup Solver

_cleanup(
  self
)

Runs cleanup code here, or code to be executed at the exit of a 'with' context statement.

# _get_next_action DeterministicPolicies

_get_next_action(
  self,
  observation: StrDict[D.T_observation]
) -> StrDict[list[D.T_event]]

Get the next deterministic action (from the solver's current policy).

# Parameters

  • observation: The observation for which next action is requested.

# Returns

The next deterministic action.

# _get_next_action_distribution UncertainPolicies

_get_next_action_distribution(
  self,
  observation: StrDict[D.T_observation]
) -> Distribution[StrDict[list[D.T_event]]]

Get the probabilistic distribution of next action for the given observation (from the solver's current policy).

# Parameters

  • observation: The observation to consider.

# Returns

The probabilistic distribution of next action.

# _initialize Solver

_initialize(
  self
)

Runs long-lasting initialization code here.

# _is_policy_defined_for Policies

_is_policy_defined_for(
  self,
  observation: StrDict[D.T_observation]
) -> bool

Check whether the solver's current policy is defined for the given observation.

# Parameters

  • observation: The observation to consider.

# Returns

True if the policy is defined for the given observation memory (False otherwise).

# _reset Solver

_reset(
  self
) -> None

Reset whatever is needed on this solver before running a new episode.

This function does nothing by default but can be overridden if needed (e.g. to reset the hidden state of a LSTM policy network, which carries information about past observations seen in the previous episode).

# _sample_action Policies

_sample_action(
  self,
  observation: StrDict[D.T_observation]
) -> StrDict[list[D.T_event]]

Sample an action for the given observation (from the solver's current policy).

# Parameters

  • observation: The observation for which an action must be sampled.

# Returns

The sampled action.

# _solve FromInitialState

_solve(
  self
) -> None

Run the solving process.

TIP

The nature of the solutions produced here depends on other solver's characteristics like policy and assessibility.